In this research project we investigate the role of responses to anomalous data during modelling processes. Modelling is seen as a comprehensive practice that encompasses various aspects of scientific thinking; hence, it is an important style of scientific thinking, especially if analysed from a process-based perspective. Therefore, it provides the opportunity to understand the role of anomalous data on scientific thinking from a broader perspective. We analysed how pre-service biology teachers (N = 11) reacted to self-generated anomalous data during modelling processes induced by investigating a water black box. The videotaped and transcribed modelling processes were analysed using qualitative content analysis. If anomalous data were recognised, a majority of explanations were based on methodical issues. This finding supports results from previous studies investigating responses to first-hand anomalous data. Furthermore, we found four response patterns to anomalous data during modelling processes: no recognition, no explanation, methodical explanation, and model-related explanation. Besides, our study indicates by trend a systematic relation between response patterns to anomalous data and modelling strategies. Consequently, the improvement of responses to anomalous data could be a promising way to foster modelling competencies. We are convinced that an integrated approach to anomalous data and modelling could lead to deeper insights into the role of data in scientific thinking processes.

在这个研究项目中，我们调查了在建模过程中对异常数据的响应的作用。建模被视为一种综合实践，涵盖了科学思维的各个方面；因此，它是一种重要的科学思维方式，尤其是从基于过程的角度进行分析时。因此，它提供了从更广泛的角度理解异常数据对科学思维的作用的机会。我们分析了职前生物教师 ( N = 11) 在建模过程中对自生成的异常数据做出反应，这些数据是通过调查水黑匣子引起的。使用定性内容分析对录像和转录的建模过程进行了分析。如果发现异常数据，大多数解释都是基于有条不紊的问题。这一发现支持先前调查对第一手异常数据的反应的研究结果。此外，我们在建模过程中发现了四种对异常数据的响应模式：不识别、不解释、有条不紊的解释和模型相关的解释。. 此外，我们的研究通过趋势表明对异常数据的响应模式与建模策略之间存在系统关系。因此，改进对异常数据的响应可能是培养建模能力的一种很有前途的方法。我们相信，异常数据和建模的综合方法可以更深入地了解数据在科学思维过程中的作用。